We give more results about the toy example detailed in sec:experiments. We only compare ASkewSGD and BinaryConnect [1]} in a logistic regression problem, but we test several settings to highlight the strengths of ASkewSGD : all methods are trained for a longer time (50 epochs) using the SGD optimizer, the learning rate is set to 1, and gradients are calculated on random batches of 100 or 1000 samples. Note the rest of the experimental setting is identical: we generate \(n=6000\)  feature vectors \(\lbrace x_k\rbrace _{k=1}^n\)  in dimension \(d=10\)  drawn independently from the uniform distribution in \([-1,1]\) . We randomly choose an optimal vector \(w_*\)  on the vertices of the hypercube and generate the labels as follows: \(y_k \sim \operatorname{Bernoulli}(\lbrace 1+\mathrm {e}^{-x_k^\top w_*}\rbrace ^{-1})\) .
For completeness, we study how a full precision SGD converges to the optimal point \(w_*\)  of this convex problem.
<FIGURE>